IoT-based Predictive Maintenance for Smart Manufacturing Systems

Manufacturers have been practicing traditional preventive maintenance for many years. However, it is not cost-effective. To avoid ineffective maintenance routine and costs, manufacturers can leverage Industrial IoT and data science. This paper presents a method to optimize the manufacturing processes by using IoT-based predictive maintenance. It illustrates how an IIoT solution can be used to predict a manufacturing defect. The data is collected from multiple smart sensors stored on this welding machine. It is monitored using Statistical process control methods. Machine learning algorithms are applied to reveal hidden correlations in the data sets and detect abnormal data patterns. The recognized data patterns are then reflected in predictive models, classification approaches are used to identify the type of manufacturing processes, namely normal and welding problem. The variables that contribute the most to the failure are identified.